gradio-medical-image-analyzer 0.1.0

AI-agent optimized medical image analysis component for Gradio with DICOM support

---

title: Medical Image Analyzer Component emoji: 🏥 colorFrom: blue colorTo: green sdk: gradio sdk_version: 5.33.0 app_file: app.py pinned: false license: apache-2.0 tags:

• custom-component-track
• medical-imaging
• gradio-custom-component
• hackathon-2025
• ai-agents

---

gradio_medical_image_analyzer

AI-agent optimized medical image analysis component for Gradio with DICOM support

Installation

pip install gradio_medical_image_analyzer

Usage

#!/usr/bin/env python3
"""
Demo for MedicalImageAnalyzer - Enhanced with file upload and overlay visualization
"""

import gradio as gr
import numpy as np
import sys
import os
import cv2
from pathlib import Path

# Add backend to path
sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(__file__)), 'backend'))

from gradio_medical_image_analyzer import MedicalImageAnalyzer

def draw_roi_on_image(image, roi_x, roi_y, roi_radius):
    """Draw ROI circle on the image"""
    # Convert to RGB if grayscale
    if len(image.shape) == 2:
        image_rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
    else:
        image_rgb = image.copy()
    
    # Draw ROI circle
    center = (int(roi_x), int(roi_y))
    radius = int(roi_radius)
    
    # Draw outer circle (white)
    cv2.circle(image_rgb, center, radius, (255, 255, 255), 2)
    # Draw inner circle (red)
    cv2.circle(image_rgb, center, radius-1, (255, 0, 0), 2)
    # Draw center cross
    cv2.line(image_rgb, (center[0]-5, center[1]), (center[0]+5, center[1]), (255, 0, 0), 2)
    cv2.line(image_rgb, (center[0], center[1]-5), (center[0], center[1]+5), (255, 0, 0), 2)
    
    return image_rgb

def create_fat_overlay(base_image, segmentation_results):
    """Create overlay image with fat segmentation highlighted"""
    # Convert to RGB
    if len(base_image.shape) == 2:
        overlay_img = cv2.cvtColor(base_image, cv2.COLOR_GRAY2RGB)
    else:
        overlay_img = base_image.copy()
    
    # Check if we have segmentation masks
    if not segmentation_results or 'segments' not in segmentation_results:
        return overlay_img
    
    segments = segmentation_results.get('segments', {})
    
    # Apply subcutaneous fat overlay (yellow)
    if 'subcutaneous' in segments and segments['subcutaneous'].get('mask') is not None:
        mask = segments['subcutaneous']['mask']
        yellow_overlay = np.zeros_like(overlay_img)
        yellow_overlay[mask > 0] = [255, 255, 0]  # Yellow
        overlay_img = cv2.addWeighted(overlay_img, 0.7, yellow_overlay, 0.3, 0)
    
    # Apply visceral fat overlay (red)
    if 'visceral' in segments and segments['visceral'].get('mask') is not None:
        mask = segments['visceral']['mask']
        red_overlay = np.zeros_like(overlay_img)
        red_overlay[mask > 0] = [255, 0, 0]  # Red
        overlay_img = cv2.addWeighted(overlay_img, 0.7, red_overlay, 0.3, 0)
    
    # Add legend
    cv2.putText(overlay_img, "Yellow: Subcutaneous Fat", (10, 30), 
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
    cv2.putText(overlay_img, "Red: Visceral Fat", (10, 60), 
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
    
    return overlay_img

def process_and_analyze(file_obj, modality, task, roi_x, roi_y, roi_radius, symptoms, show_overlay=False):
    """
    Processes uploaded file and performs analysis
    """
    if file_obj is None:
        return None, "No file selected", None, {}, None
    
    # Create analyzer instance
    analyzer = MedicalImageAnalyzer(
        analysis_mode="structured",
        include_confidence=True,
        include_reasoning=True
    )
    
    try:
        # Process the file (DICOM or image)
        file_path = file_obj.name if hasattr(file_obj, 'name') else str(file_obj)
        pixel_array, display_array, metadata = analyzer.process_file(file_path)
        
        # Update modality from file metadata if it's a DICOM
        if metadata.get('file_type') == 'DICOM' and 'modality' in metadata:
            modality = metadata['modality']
        
        # Prepare analysis parameters
        analysis_params = {
            "image": pixel_array,
            "modality": modality,
            "task": task
        }
        
        # Add ROI if applicable
        if task in ["analyze_point", "full_analysis"]:
            # Scale ROI coordinates to image size
            h, w = pixel_array.shape
            roi_x_scaled = int(roi_x * w / 512)  # Assuming slider max is 512
            roi_y_scaled = int(roi_y * h / 512)
            
            analysis_params["roi"] = {
                "x": roi_x_scaled,
                "y": roi_y_scaled,
                "radius": roi_radius
            }
        
        # Add clinical context
        if symptoms:
            analysis_params["clinical_context"] = {"symptoms": symptoms}
        
        # Perform analysis
        results = analyzer.analyze_image(**analysis_params)
        
        # Create visual report
        visual_report = create_visual_report(results, metadata)
        
        # Add metadata info
        info = f"📄 {metadata.get('file_type', 'Unknown')} | "
        info += f"🏥 {modality} | "
        info += f"📐 {metadata.get('shape', 'Unknown')}"
        
        if metadata.get('window_center'):
            info += f" | Window C:{metadata['window_center']:.0f} W:{metadata['window_width']:.0f}"
        
        # Create overlay image if requested
        overlay_image = None
        if show_overlay:
            # For ROI visualization
            if task in ["analyze_point", "full_analysis"] and roi_x and roi_y:
                overlay_image = draw_roi_on_image(display_array.copy(), roi_x_scaled, roi_y_scaled, roi_radius)
            
            # For fat segmentation overlay (simplified version since we don't have masks in current implementation)
            elif task == "segment_fat" and 'segmentation' in results and modality == 'CT':
                # For now, just draw ROI since we don't have actual masks
                overlay_image = display_array.copy()
                if len(overlay_image.shape) == 2:
                    overlay_image = cv2.cvtColor(overlay_image, cv2.COLOR_GRAY2RGB)
                # Add text overlay about fat percentages
                if 'statistics' in results['segmentation']:
                    stats = results['segmentation']['statistics']
                    cv2.putText(overlay_image, f"Total Fat: {stats.get('total_fat_percentage', 0):.1f}%", 
                               (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
                    cv2.putText(overlay_image, f"Subcutaneous: {stats.get('subcutaneous_fat_percentage', 0):.1f}%", 
                               (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
                    cv2.putText(overlay_image, f"Visceral: {stats.get('visceral_fat_percentage', 0):.1f}%", 
                               (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
        
        return display_array, info, visual_report, results, overlay_image
        
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        return None, error_msg, f"<div style='color: red;'>❌ {error_msg}</div>", {"error": error_msg}, None

def create_visual_report(results, metadata):
    """Creates a visual HTML report with improved styling"""
    html = f"""
    <div class='medical-report' style='font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif; 
                padding: 24px; 
                background: #ffffff; 
                border-radius: 12px; 
                max-width: 100%; 
                box-shadow: 0 2px 8px rgba(0,0,0,0.1);
                color: #1a1a1a !important;'>
        
        <h2 style='color: #1e40af !important; 
                   border-bottom: 3px solid #3b82f6; 
                   padding-bottom: 12px; 
                   margin-bottom: 20px;
                   font-size: 24px;
                   font-weight: 600;'>
            🏥 Medical Image Analysis Report
        </h2>
        
        <div style='background: #f0f9ff; 
                    padding: 20px; 
                    margin: 16px 0; 
                    border-radius: 8px; 
                    box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
            <h3 style='color: #1e3a8a !important; 
                       font-size: 18px; 
                       font-weight: 600; 
                       margin-bottom: 12px;'>
                📋 Metadata
            </h3>
            <table style='width: 100%; border-collapse: collapse;'>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important; width: 40%;'><strong style='color: #374151 !important;'>File Type:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{metadata.get('file_type', 'Unknown')}</td>
                </tr>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Modality:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{results.get('modality', 'Unknown')}</td>
                </tr>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Image Size:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{metadata.get('shape', 'Unknown')}</td>
                </tr>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Timestamp:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{results.get('timestamp', 'N/A')}</td>
                </tr>
            </table>
        </div>
    """
    
    # Point Analysis
    if 'point_analysis' in results:
        pa = results['point_analysis']
        tissue = pa.get('tissue_type', {})
        
        html += f"""
        <div style='background: #f0f9ff; 
                    padding: 20px; 
                    margin: 16px 0; 
                    border-radius: 8px; 
                    box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
            <h3 style='color: #1e3a8a !important; 
                       font-size: 18px; 
                       font-weight: 600; 
                       margin-bottom: 12px;'>
                🎯 Point Analysis
            </h3>
            <table style='width: 100%; border-collapse: collapse;'>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important; width: 40%;'><strong style='color: #374151 !important;'>Position:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>({pa.get('location', {}).get('x', 'N/A')}, {pa.get('location', {}).get('y', 'N/A')})</td>
                </tr>
        """
        
        if results.get('modality') == 'CT':
            html += f"""
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>HU Value:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important; font-weight: 500;'>{pa.get('hu_value', 'N/A'):.1f}</td>
                </tr>
            """
        else:
            html += f"""
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Intensity:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{pa.get('intensity', 'N/A'):.3f}</td>
                </tr>
            """
        
        html += f"""
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Tissue Type:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>
                        <span style='font-size: 1.3em; vertical-align: middle;'>{tissue.get('icon', '')}</span> 
                        <span style='font-weight: 500; text-transform: capitalize;'>{tissue.get('type', 'Unknown').replace('_', ' ')}</span>
                    </td>
                </tr>
                <tr>
                    <td style='padding: 8px 0; color: #4b5563 !important;'><strong style='color: #374151 !important;'>Confidence:</strong></td>
                    <td style='padding: 8px 0; color: #1f2937 !important;'>{pa.get('confidence', 'N/A')}</td>
                </tr>
            </table>
        """
        
        if 'reasoning' in pa:
            html += f"""
            <div style='margin-top: 12px; 
                        padding: 12px; 
                        background: #dbeafe; 
                        border-left: 3px solid #3b82f6; 
                        border-radius: 4px;'>
                <p style='margin: 0; color: #1e40af !important; font-style: italic;'>
                    💭 {pa['reasoning']}
                </p>
            </div>
            """
        
        html += "</div>"
    
    # Segmentation Results
    if 'segmentation' in results and results['segmentation']:
        seg = results['segmentation']
        
        if 'statistics' in seg:
            # Fat segmentation for CT
            stats = seg['statistics']
            html += f"""
            <div style='background: #f0f9ff; 
                        padding: 20px; 
                        margin: 16px 0; 
                        border-radius: 8px; 
                        box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
                <h3 style='color: #1e3a8a !important; 
                           font-size: 18px; 
                           font-weight: 600; 
                           margin-bottom: 12px;'>
                    🔬 Fat Segmentation Analysis
                </h3>
                <div style='display: grid; grid-template-columns: 1fr 1fr; gap: 16px;'>
                    <div style='padding: 16px; background: #ffffff; border-radius: 6px; border: 1px solid #e5e7eb;'>
                        <h4 style='color: #6b7280 !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Total Fat</h4>
                        <p style='color: #1f2937 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('total_fat_percentage', 0):.1f}%</p>
                    </div>
                    <div style='padding: 16px; background: #fffbeb; border-radius: 6px; border: 1px solid #fbbf24;'>
                        <h4 style='color: #92400e !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Subcutaneous</h4>
                        <p style='color: #d97706 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('subcutaneous_fat_percentage', 0):.1f}%</p>
                    </div>
                    <div style='padding: 16px; background: #fef2f2; border-radius: 6px; border: 1px solid #fca5a5;'>
                        <h4 style='color: #991b1b !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>Visceral</h4>
                        <p style='color: #dc2626 !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('visceral_fat_percentage', 0):.1f}%</p>
                    </div>
                    <div style='padding: 16px; background: #eff6ff; border-radius: 6px; border: 1px solid #93c5fd;'>
                        <h4 style='color: #1e3a8a !important; font-size: 14px; margin: 0 0 8px 0; font-weight: 500;'>V/S Ratio</h4>
                        <p style='color: #1e40af !important; font-size: 24px; font-weight: 600; margin: 0;'>{stats.get('visceral_subcutaneous_ratio', 0):.2f}</p>
                    </div>
                </div>
            """
            
            if 'interpretation' in seg:
                interp = seg['interpretation']
                obesity_color = "#16a34a" if interp.get("obesity_risk") == "normal" else "#d97706" if interp.get("obesity_risk") == "moderate" else "#dc2626"
                visceral_color = "#16a34a" if interp.get("visceral_risk") == "normal" else "#d97706" if interp.get("visceral_risk") == "moderate" else "#dc2626"
                
                html += f"""
                <div style='margin-top: 16px; padding: 16px; background: #f3f4f6; border-radius: 6px;'>
                    <h4 style='color: #374151 !important; font-size: 16px; font-weight: 600; margin-bottom: 8px;'>Risk Assessment</h4>
                    <div style='display: grid; grid-template-columns: 1fr 1fr; gap: 12px;'>
                        <div>
                            <span style='color: #6b7280 !important; font-size: 14px;'>Obesity Risk:</span>
                            <span style='color: {obesity_color} !important; font-weight: 600; margin-left: 8px;'>{interp.get('obesity_risk', 'N/A').upper()}</span>
                        </div>
                        <div>
                            <span style='color: #6b7280 !important; font-size: 14px;'>Visceral Risk:</span>
                            <span style='color: {visceral_color} !important; font-weight: 600; margin-left: 8px;'>{interp.get('visceral_risk', 'N/A').upper()}</span>
                        </div>
                    </div>
                """
                
                if interp.get('recommendations'):
                    html += """
                    <div style='margin-top: 12px; padding-top: 12px; border-top: 1px solid #e5e7eb;'>
                        <h5 style='color: #374151 !important; font-size: 14px; font-weight: 600; margin-bottom: 8px;'>💡 Recommendations</h5>
                        <ul style='margin: 0; padding-left: 20px; color: #4b5563 !important;'>
                    """
                    for rec in interp['recommendations']:
                        html += f"<li style='margin: 4px 0;'>{rec}</li>"
                    html += "</ul></div>"
                
                html += "</div>"
            html += "</div>"
    
    # Quality Assessment
    if 'quality_metrics' in results:
        quality = results['quality_metrics']
        quality_colors = {
            'excellent': '#16a34a',
            'good': '#16a34a',
            'fair': '#d97706',
            'poor': '#dc2626',
            'unknown': '#6b7280'
        }
        q_color = quality_colors.get(quality.get('overall_quality', 'unknown'), '#6b7280')
        
        html += f"""
        <div style='background: #f0f9ff; 
                    padding: 20px; 
                    margin: 16px 0; 
                    border-radius: 8px; 
                    box-shadow: 0 1px 3px rgba(0,0,0,0.1);'>
            <h3 style='color: #1e3a8a !important; 
                       font-size: 18px; 
                       font-weight: 600; 
                       margin-bottom: 12px;'>
                📊 Image Quality Assessment
            </h3>
            <div style='display: flex; align-items: center; gap: 16px;'>
                <div>
                    <span style='color: #4b5563 !important; font-size: 14px;'>Overall Quality:</span>
                    <span style='color: {q_color} !important; 
                                 font-size: 18px; 
                                 font-weight: 700; 
                                 margin-left: 8px;'>
                        {quality.get('overall_quality', 'unknown').upper()}
                    </span>
                </div>
            </div>
        """
        
        if quality.get('issues'):
            html += f"""
            <div style='margin-top: 12px; 
                        padding: 12px; 
                        background: #fef3c7; 
                        border-left: 3px solid #f59e0b; 
                        border-radius: 4px;'>
                <strong style='color: #92400e !important;'>Issues Detected:</strong>
                <ul style='margin: 4px 0 0 0; padding-left: 20px; color: #92400e !important;'>
            """
            for issue in quality['issues']:
                html += f"<li style='margin: 2px 0;'>{issue}</li>"
            html += "</ul></div>"
        
        html += "</div>"
    
    html += "</div>"
    return html

def create_demo():
    with gr.Blocks(
        title="Medical Image Analyzer - Enhanced Demo", 
        theme=gr.themes.Soft(
            primary_hue="blue",
            secondary_hue="blue",
            neutral_hue="slate",
            text_size="md",
            spacing_size="md",
            radius_size="md",
        ).set(
            # Medical blue theme colors
            body_background_fill="*neutral_950",
            body_background_fill_dark="*neutral_950",
            block_background_fill="*neutral_900",
            block_background_fill_dark="*neutral_900",
            border_color_primary="*primary_600",
            border_color_primary_dark="*primary_600",
            # Text colors for better contrast
            body_text_color="*neutral_100",
            body_text_color_dark="*neutral_100",
            body_text_color_subdued="*neutral_300",
            body_text_color_subdued_dark="*neutral_300",
            # Button colors
            button_primary_background_fill="*primary_600",
            button_primary_background_fill_dark="*primary_600",
            button_primary_text_color="white",
            button_primary_text_color_dark="white",
        ),
        css="""
        /* Medical blue theme with high contrast */
        :root {
            --medical-blue: #1e40af;
            --medical-blue-light: #3b82f6;
            --medical-blue-dark: #1e3a8a;
            --text-primary: #f9fafb;
            --text-secondary: #e5e7eb;
            --bg-primary: #0f172a;
            --bg-secondary: #1e293b;
            --bg-tertiary: #334155;
        }
        
        /* Override default text colors for medical theme */
        * {
            color: var(--text-primary) !important;
        }
        
        /* Style the file upload area */
        .file-upload {
            border: 2px dashed var(--medical-blue-light) !important;
            border-radius: 8px !important;
            padding: 20px !important;
            text-align: center !important;
            background: var(--bg-secondary) !important;
            transition: all 0.3s ease !important;
            color: var(--text-primary) !important;
        }
        
        .file-upload:hover {
            border-color: var(--medical-blue) !important;
            background: var(--bg-tertiary) !important;
            box-shadow: 0 0 20px rgba(59, 130, 246, 0.2) !important;
        }
        
        /* Ensure report text is readable with white background */
        .medical-report {
            background: #ffffff !important;
            border: 2px solid var(--medical-blue-light) !important;
            border-radius: 8px !important;
            padding: 16px !important;
            color: #1a1a1a !important;
        }
        
        .medical-report * {
            color: #1f2937 !important; /* Dark gray text */
        }
        
        .medical-report h2 {
            color: #1e40af !important; /* Medical blue for main heading */
        }
        
        .medical-report h3, .medical-report h4 {
            color: #1e3a8a !important; /* Darker medical blue for subheadings */
        }
        
        .medical-report strong {
            color: #374151 !important; /* Darker gray for labels */
        }
        
        .medical-report td {
            color: #1f2937 !important; /* Ensure table text is dark */
        }
        
        /* Report sections with light blue background */
        .medical-report > div {
            background: #f0f9ff !important;
            color: #1f2937 !important;
        }
        
        /* Medical blue accents for UI elements */
        .gr-button-primary {
            background: var(--medical-blue) !important;
            border-color: var(--medical-blue) !important;
        }
        
        .gr-button-primary:hover {
            background: var(--medical-blue-dark) !important;
            border-color: var(--medical-blue-dark) !important;
        }
        
        /* Tab styling */
        .gr-tab-item {
            border-color: var(--medical-blue-light) !important;
        }
        
        .gr-tab-item.selected {
            background: var(--medical-blue) !important;
            color: white !important;
        }
        
        /* Accordion styling */
        .gr-accordion {
            border-color: var(--medical-blue-light) !important;
        }
        
        /* Slider track in medical blue */
        input[type="range"]::-webkit-slider-track {
            background: var(--bg-tertiary) !important;
        }
        
        input[type="range"]::-webkit-slider-thumb {
            background: var(--medical-blue) !important;
        }
        """
    ) as demo:
        gr.Markdown("""
        # 🏥 Medical Image Analyzer
        
        Supports **DICOM** (.dcm) and all image formats with automatic modality detection!
        """)
        
        with gr.Row():
            with gr.Column(scale=1):
                # File upload - no file type restrictions
                with gr.Group():
                    gr.Markdown("### 📤 Upload Medical Image")
                    file_input = gr.File(
                        label="Select Medical Image File (.dcm, .dicom, IM_*, .png, .jpg, etc.)",
                        file_count="single",
                        type="filepath",
                        elem_classes="file-upload"
                        # Note: NO file_types parameter = accepts ALL files
                    )
                    gr.Markdown("""
                    <small style='color: #666;'>
                    Accepts: DICOM (.dcm, .dicom), Images (.png, .jpg, .jpeg, .tiff, .bmp), 
                    and files without extensions (e.g., IM_0001, IM_0002, etc.)
                    </small>
                    """)
                
                # Modality selection
                modality = gr.Radio(
                    choices=["CT", "CR", "DX", "RX", "DR"],
                    value="CT",
                    label="Modality",
                    info="Will be auto-detected for DICOM files"
                )
                
                # Task selection
                task = gr.Dropdown(
                    choices=[
                        ("🎯 Point Analysis", "analyze_point"),
                        ("🔬 Fat Segmentation (CT only)", "segment_fat"),
                        ("📊 Full Analysis", "full_analysis")
                    ],
                    value="full_analysis",
                    label="Analysis Task"
                )
                
                # ROI settings
                with gr.Accordion("🎯 Region of Interest (ROI)", open=True):
                    roi_x = gr.Slider(0, 512, 256, label="X Position", step=1)
                    roi_y = gr.Slider(0, 512, 256, label="Y Position", step=1)
                    roi_radius = gr.Slider(5, 50, 10, label="Radius", step=1)
                
                # Clinical context
                with gr.Accordion("🏥 Clinical Context", open=False):
                    symptoms = gr.CheckboxGroup(
                        choices=[
                            "dyspnea", "chest_pain", "abdominal_pain",
                            "trauma", "obesity_screening", "routine_check"
                        ],
                        label="Symptoms/Indication"
                    )
                
                # Visualization options
                with gr.Accordion("🎨 Visualization Options", open=True):
                    show_overlay = gr.Checkbox(
                        label="Show ROI/Segmentation Overlay",
                        value=True,
                        info="Display ROI circle or fat segmentation info on the image"
                    )
                
                analyze_btn = gr.Button("🔬 Analyze", variant="primary", size="lg")
            
            with gr.Column(scale=2):
                # Results with tabs for different views
                with gr.Tab("🖼️ Original Image"):
                    image_display = gr.Image(label="Medical Image", type="numpy")
                    
                with gr.Tab("🎯 Overlay View"):
                    overlay_display = gr.Image(label="Image with Overlay", type="numpy")
                
                file_info = gr.Textbox(label="File Information", lines=1)
                
                with gr.Tab("📊 Visual Report"):
                    report_html = gr.HTML()
                
                with gr.Tab("🔧 JSON Output"):
                    json_output = gr.JSON(label="Structured Data for AI Agents")
        
        # Examples and help
        with gr.Row():
            gr.Markdown("""
            ### 📁 Supported Formats
            - **DICOM**: Automatic HU value extraction and modality detection
            - **PNG/JPG**: Interpreted based on selected modality
            - **All Formats**: Automatic grayscale conversion
            - **Files without extension**: Supported (e.g., IM_0001) - will try DICOM first
            
            ### 🎯 Usage
            1. Upload a medical image file
            2. Select modality (auto-detected for DICOM)
            3. Choose analysis task
            4. Adjust ROI position for point analysis
            5. Click "Analyze"
            
            ### 💡 Features
            - **ROI Visualization**: See the exact area being analyzed
            - **Fat Segmentation**: Visual percentages for CT scans
            - **Multi-format Support**: Works with any medical image format
            - **AI Agent Ready**: Structured JSON output for integration
            """)
        
        # Connect the interface
        analyze_btn.click(
            fn=process_and_analyze,
            inputs=[file_input, modality, task, roi_x, roi_y, roi_radius, symptoms, show_overlay],
            outputs=[image_display, file_info, report_html, json_output, overlay_display]
        )
        
        # Auto-update ROI limits when image is loaded
        def update_roi_on_upload(file_obj):
            if file_obj is None:
                return gr.update(), gr.update()
            
            try:
                analyzer = MedicalImageAnalyzer()
                _, _, metadata = analyzer.process_file(file_obj.name if hasattr(file_obj, 'name') else str(file_obj))
                
                if 'shape' in metadata:
                    h, w = metadata['shape']
                    return gr.update(maximum=w-1, value=w//2), gr.update(maximum=h-1, value=h//2)
            except:
                pass
            
            return gr.update(), gr.update()
        
        file_input.change(
            fn=update_roi_on_upload,
            inputs=[file_input],
            outputs=[roi_x, roi_y]
        )
    
    return demo

if __name__ == "__main__":
    demo = create_demo()
    demo.launch()

MedicalImageAnalyzer

Initialization

name	type	default	description
value	typing.Optional[typing.Dict[str, typing.Any]][ typing.Dict[str, typing.Any][str, typing.Any], None ]	None	None
label	typing.Optional[str][str, None]	None	None
info	typing.Optional[str][str, None]	None	None
every	typing.Optional[float][float, None]	None	None
show_label	typing.Optional[bool][bool, None]	None	None
container	typing.Optional[bool][bool, None]	None	None
scale	typing.Optional[int][int, None]	None	None
min_width	typing.Optional[int][int, None]	None	None
visible	typing.Optional[bool][bool, None]	None	None
elem_id	typing.Optional[str][str, None]	None	None
elem_classes	typing.Union[typing.List[str], str, NoneType][ typing.List[str][str], str, None ]	None	None
render	typing.Optional[bool][bool, None]	None	None
key	typing.Union[int, str, NoneType][int, str, None]	None	None
analysis_mode	str	"structured"	"structured" for AI agents, "visual" for human interpretation
include_confidence	bool	True	Include confidence scores in results
include_reasoning	bool	True	Include reasoning/explanation for findings
segmentation_types	typing.List[str][str]	None	List of segmentation types to perform

Events

name	description
change	Triggered when the value of the MedicalImageAnalyzer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See .input() for a listener that is only triggered by user input.
select	Event listener for when the user selects or deselects the MedicalImageAnalyzer. Uses event data gradio.SelectData to carry value referring to the label of the MedicalImageAnalyzer, and selected to refer to state of the MedicalImageAnalyzer. See EventData documentation on how to use this event data
upload	This listener is triggered when the user uploads a file into the MedicalImageAnalyzer.
clear	This listener is triggered when the user clears the MedicalImageAnalyzer using the clear button for the component.

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

• When used as an Input, the component only impacts the input signature of the user function.
• When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

def predict(
    value: typing.Dict[str, typing.Any][str, typing.Any]
) -> typing.Dict[str, typing.Any][str, typing.Any]:
    return value